class_docstr: "RetinaHead with separate BN.
  
  In RetinaHead, conv/norm layers are shared across different FPN levels,
  while in RetinaSepBNHead, conv layers are shared across different FPN
  levels, but BN layers are separated.
docstr: Forward features from the upstream network.
  
  Args:
      feats (tuple[Tensor]): Features from the upstream network, each is
          a 4D-tensor.
  
  Returns:
      tuple: Usually a tuple of classification scores and bbox prediction
          cls_scores (list[Tensor]): Classification scores for all scale
              levels, each is a 4D-tensor, the channels number is
              num_anchors * num_classes.
          bbox_preds (list[Tensor]): Box energies / deltas for all scale
              levels, each is a 4D-tensor, the channels number is
              num_anchors * 4.
docstr_tok: ["Forward","features","from","the","upstream","network",".","Args",":","feats","(","tuple","[","Tensor","]",")",":","Features","from","the","upstream","network",",","each","is","a","4D-tensor",".","Returns",":","tuple",":","Usually","a","tuple","of","classification","scores","and","bbox","prediction","cls_scores","(","list","[","Tensor","]",")",":","Classification","scores","for","all","scale","levels",",","each","is","a","4D-tensor",",","the","channels","number","is","num_anchors","*","num_classes",".","bbox_preds","(","list","[","Tensor","]",")",":","Box","energies","\/","deltas","for","all","scale","levels",",","each","is","a","4D-tensor",",","the","channels","number","is","num_anchors","*","4","."]
code: def forward(self, feats):
      """Forward features from the upstream network.
  
      Args:
          feats (tuple[Tensor]): Features from the upstream network, each is
              a 4D-tensor.
  
      Returns:
          tuple: Usually a tuple of classification scores and bbox prediction
              cls_scores (list[Tensor]): Classification scores for all scale
                  levels, each is a 4D-tensor, the channels number is
                  num_anchors * num_classes.
              bbox_preds (list[Tensor]): Box energies / deltas for all scale
                  levels, each is a 4D-tensor, the channels number is
                  num_anchors * 4.
      """
      cls_scores = []
      bbox_preds = []
      for i, x in enumerate(feats):
          cls_feat = feats[i]
          reg_feat = feats[i]
          for cls_conv in self.cls_convs[i]:
              cls_feat = cls_conv(cls_feat)
          for reg_conv in self.reg_convs[i]:
              reg_feat = reg_conv(reg_feat)
          cls_score = self.retina_cls(cls_feat)
          bbox_pred = self.retina_reg(reg_feat)
          cls_scores.append(cls_score)
          bbox_preds.append(bbox_pred)
      return cls_scores, bbox_preds
  
file_path: insightface/detection/scrfd/mmdet/models/dense_heads/retina_sepbn_head.py
imports: import torch.nn
  from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
  from ..builder import HEADS
  from .anchor_head import AnchorHead
label_desc: Use image node_id 4 for calling the RetinaSepBNHead obj's underlying member method code with example usage: obj.forward(feats) and returns: cls_scores,  bbox_preds